AUTHOR=Daly Mark , Aslam Muhammad Ahtisham , Froelian Ronja , Schimmler Sonja 
  
TITLE=OntoTrack: a linked open data based solution to track mutual citation networks in research publications
  
JOURNAL=Frontiers in Computer Science
  
VOLUME=Volume 8 - 2026
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2026.1636097
  
DOI=10.3389/fcomp.2026.1636097
  
ISSN=2624-9898
  
ABSTRACT=ContextScientific publications are vital for a researcher's scientific career. Citations of scientific work by other researchers are considered as evidence of scientific and technical strength and acceptance of one's scientific contributions. The higher citation count directly raises the “h-index,” which is evidence of a strong scientific profile. Due to its direct impact on scientific profiles, citation manipulation (unnatural citations) is becoming a major concern in academia and industry.MethodsTo address this challenge, we present OntoTrack, an ontology-based solution that can be used to detect and identify potential unnatural citations and citation networks within the scientific literature. In this paper, we present the complete architecture of the OntoTrack solution. We also present the OntoTrack data model and ontology with its key attributes and parameters, which play an important role in tracking citation networks. OntoTrack ontology is equipped with a comprehensive set of rules that are defined using the Semantic Web Rule Language (SWRL). These rules enhance the reasoning capabilities of OntoTrack and facilitate smart identification of unnatural citation indicators. A proof-of-concept dataset is produced as part of this work and used to evaluate the effectiveness and precision of the OntoTrack Solution in detecting citation anomalies.ResultsWe also present the evaluation of the OntoTrack Solution by defining a comprehensive set of Competency Questions (CQs) and executing these against the OntoTrack SPARQL Protocol and RDF Query Language (SPARQL) Endpoint. The results of the evaluations show that OntoTrack can successfully identify various forms of unnatural citations, including self-citations, citation cartels, and citation manipulation among researchers. The results also show that an ontology-based approach provides a sustainable and efficient alternative to traditional machine-learning methods, which often require extensive computational resources.DiscussionThe findings suggest that ontology-based systems such as OntoTrack can enhance transparency and integrity in academic research by providing a robust mechanism for monitoring citation practices.